• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.61-62
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• 2006

A new neural network classifier is proposed for the automatic real-time surface inspection of high-speed cold steel strips having 11 different types of defects. 46 geometrical and gray-level features are extracted for the defect classification. 3241 samples of Posco's Kwangyang steel factory are used for training and testing the neural network classifier. The developed classifier produces plausible 15% error rate which is much better than 20-30% error rate of human vision inspection adopted in most of domestic steel factories.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.4
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• pp.112-117
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• 2003

This paper deals with the problem of synthesis of the 3-dimensional Grasp Planning. In previous studies the genetic algorithm has been used to find optimal grasping points, but it had a limitation such as the determination time of grasping points was so long. To overcome this limitation we proposed a new algorithm which employs the Neural Network. In the Neural network we chose input parameters based on the shape of the object and output parameters resulted from optimization with the GA method. In this study the GRNN method is employed, it has been trained by the result value of optimization method and it has been tested by known object. The algorithm is verified by computer simulation.

• Journal of Welding and Joining
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• v.31 no.3
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• pp.44-48
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• 2013

Artificial neural network (ANN) model is applied to predict arc welding process window for automotive steel plate. Target weldment was various automotive steel plate combination with lap fillet joint. The accuracy of prediction was evaluated through comparison experimental result to ANN simulation. The effect of ANN variables on the accuracy is investigated such as number of hidden layers, perceptrons and transfer function type. A static back propagation model is established and tested. The result shows comparatively accurate predictability of the suggested ANN model. However, it restricts to use nonlinear transfer function instead of linear type and suggests only one single hidden layer rather than multiple ones to get better accuracy. In addition to this, obvious fact is affirmed again that the more perceptrons guarantee the better accuracy under the precondition that there are enough experimental database to train the neural network.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.3
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• pp.14-21
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• 1998

The in-process detection of the state of cutting tool is one of the most important technical problem in Intelligent Machining System. This paper presents a method of detecting the state of cutting tool in turning process, by using Artificial Neural Network. In order to sense the state of cutting tool. the sensor fusion of an acoustic emission sensor and a force sensor is applied in this paper. It is shown that AErms and three directional dynamic mean cutting forces are sensitive to the tool wear. Therefore the six pattern features that is, the four sensory signal features and two cutting conditions are selected for the monitoring system with Artificial Neural Network. The proposed monitoring system shows a good recogniton rate for the different cutting conditions.

• Journal of the Korean Society for Heat Treatment
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• v.31 no.1
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• pp.1-5
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• 2018

This work demonstrated the application of an artificial neural network model for predicting the Jominy hardness curve by considering 13 alloying elements in low alloy steels. End-quench Jominy tests were carried out according to ASTM A255 standard method for 1197 samples. The hardness values of Jominy sample were measured at different points from the quenched end. The developed artificial neural network model predicted the Jominy curve with high accuracy ($R^2=0.9969$ for training and $R^2=0.9956$ for verification). In addition, the model was used to investigate the average sensitivity of input variables to hardness change.

• Asia pacific journal of information systems
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• v.5 no.1
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• pp.36-51
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• 1995

본 논문에서는 비구조적인 의사결정문제를 효과적으로 해결하기 위하여 감독학습 인공신경망 모형과 비감독학습 인공신경망 모형을 결합한 하이브리드 인공신경망 모형인 HYNEN(HYbrid NEural Network) 모형을 제안한다. HYNEN모형은 주어진 자료를 클러스터화 하는 CNN(Clustering Neural Network)과 최종적인 출력을 제공하는 ONN(Output Neural Network)의 2단계로 구성되어 있다. 먼저 CNN에서는 주어진 자료로부터 적정한 퍼지규칙을 찾기 위하여 클러스터를 구성한다. 그리고 이러한 클러스터를 지식베이스로하여 ONN에서 최종적인 의사결정을 한다. CNN에서는 SOFM(Self Organizing Feature Map)과 LVQ(Learning Vector Quantization)를 클러스터를 만든 후 역전파학습 인공신경망 모형으로 이를 학습한다. ONN에서는 역전파학습 인공신경망 모형을 이용하여 각 클러스터의 내용을 학습한다. 제안된 HYNEN 모형을 우리나라 기업의 도산자료에 적용하여 그 결과를 다변량 판별분석법(MDA:Multivariate Discriminant Analysis)과 ACLS(Analog Concept Learning System) 퍼지 ARTMAP 그리고 기존의 역전파학습 인공신경망에 의한 실험결과와 비교하였다.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.646-649
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• 2002

It can be effect on diagnosis of hydraulic machining system to analyze working conditions with shape characteristics of wear debris in a lubricated machine. But, in order to predict and estimate working conditions, it is need to analyze the shape characteristics of wear debris and to identify. Therefor, if shape characteristics of wear debris is identified by computer image analysis and the neural network, it is possible to find the cause and effect of moving condition. In this study, wear debris in the lubricant oil are extracted by membrane filter, and the quantitative value of shape characteristics of wear debris we calculated by the digital image processing. This morphological informations are studied and identified by the artificial neural network. The purpose of this study is In apply morphological characteristics of wear debris to prediction and estimation of working condition in hydraulic driving systems.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.168-171
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• 2002

A neural network model for predicting the quality or soundness of the injected plastic aspherical lens based on process parameters has been developed. The approach uses a Real Time Recurrent Neural Network 4-5-2 (RTRN) trained based on input/output data that were taken from FE analysis worts carried out through a CAE software. The system has been developed to search an optimum set of process parameters and reduce the time required for planning the conditions of plastic injection molding at the design stage.

• Journal of Environmental Impact Assessment
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• v.18 no.5
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• pp.271-280
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• 2009

The objective of this study is to develop a reliable flow forecasting model based on neural network algorithm in order to provide flow rate at stream sections without flow measurement in Nakdong river. Stream flow rate measured at 8-days interval by Nakdong river environment research center, daily upper dam discharge and precipitation data connecting upstream stage gauge were used in this development. Back propagation neural network and multi-layer with hidden layer that exists between input and output layer are used in model learning and constructing, respectively. Model calibration and verification is conducted based on observed data from 3 station in Nakdong river.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.8
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• pp.5219-5226
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• 2014

This paper proposes an improved scheme of pedestrian position information system using neural network theory in a GPS-disabled area. Through a learning/obtaining gait pattern and step distance about walk, run, duck walk, crab walk and crawl, the position estimation error could be minimized by rejecting the inertial navigation drift. A portable hardware module was implemented to evaluate the performance of the proposed system. The performance and effectiveness of the suggested algorithm was verified by experiments indoors.